{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:17:16Z","timestamp":1767705436954,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686318"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Few-shot semantic segmentation aims to segment objects from previously unseen classes using only a limited number of labeled examples. In this paper, we introduce Label Anything, a novel transformer-based architecture designed for multi-prompt, multi-way few-shot semantic segmentation. Our approach leverages diverse visual prompts\u2014points, bounding boxes, and masks\u2014to create a highly flexible and generalizable framework that significantly reduces annotation burden while maintaining high accuracy. Label Anything makes three key contributions: (i) we introduce a new task formulation that relaxes conventional few-shot segmentation constraints by supporting various types of prompts, multi-class classification, and enabling multiple prompts within a single image; (ii) we propose a novel architecture based on transformers and attention mechanisms; and (iii) we design a versatile training procedure allowing our model to operate seamlessly across different N-way K-shot and prompt-type configurations with a single trained model. Our extensive experimental evaluation on the widely used COCO-20i benchmark demonstrates that Label Anything achieves state-of-the-art performance among existing multi-way few-shot segmentation methods, while significantly outperforming leading single-class models when evaluated in multi-class settings. Code and trained models are available at https:\/\/github.com\/pasqualedem\/LabelAnything.<\/jats:p>","DOI":"10.3233\/faia251289","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:57:06Z","timestamp":1761127026000},"source":"Crossref","is-referenced-by-count":2,"title":["Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8935-9156","authenticated-orcid":false,"given":"Pasquale","family":"De Marinis","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6602-7504","authenticated-orcid":false,"given":"Nicola","family":"Fanelli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7512-7661","authenticated-orcid":false,"given":"Raffaele","family":"Scaringi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0932-3424","authenticated-orcid":false,"given":"Emanuele","family":"Colonna","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8687-6609","authenticated-orcid":false,"given":"Giuseppe","family":"Fiameni","sequence":"additional","affiliation":[{"name":"NVIDIA AI Technology Center, Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0883-2691","authenticated-orcid":false,"given":"Gennaro","family":"Vessio","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6489-8628","authenticated-orcid":false,"given":"Giovanna","family":"Castellano","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Bari, Italy"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251289","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T12:52:01Z","timestamp":1762519921000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251289","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}